BUG: rolling with Int64 (pandas-dev#43174)

Author: mroeschke

Diff for: doc/source/whatsnew/v1.4.0.rst

@@ -356,7 +356,7 @@ Groupby/resample/rolling
 - Bug in :meth:`pandas.DataFrame.ewm`, where non-float64 dtypes were silently failing (:issue:`42452`)
 - Bug in :meth:`pandas.DataFrame.rolling` operation along rows (``axis=1``) incorrectly omits columns containing ``float16`` and ``float32`` (:issue:`41779`)
 - Bug in :meth:`Resampler.aggregate` did not allow the use of Named Aggregation (:issue:`32803`)
--
+- Bug in :meth:`Series.rolling` when the :class:`Series` ``dtype`` was ``Int64`` (:issue:`43016`)
 
 Reshaping
 ^^^^^^^^^

Diff for: pandas/core/window/rolling.py

@@ -50,6 +50,7 @@
 
 from pandas.core.algorithms import factorize
 from pandas.core.apply import ResamplerWindowApply
+from pandas.core.arrays import ExtensionArray
 from pandas.core.base import (
     DataError,
     SelectionMixin,
@@ -317,7 +318,10 @@ def _prep_values(self, values: ArrayLike) -> np.ndarray:
             # GH #12373 : rolling functions error on float32 data
             # make sure the data is coerced to float64
             try:
-                values = ensure_float64(values)
+                if isinstance(values, ExtensionArray):
+                    values = values.to_numpy(np.float64, na_value=np.nan)
+                else:
+                    values = ensure_float64(values)
             except (ValueError, TypeError) as err:
                 raise TypeError(f"cannot handle this type -> {values.dtype}") from err
 

Diff for: pandas/tests/window/test_dtypes.py

@@ -2,6 +2,7 @@
 import pytest
 
 from pandas import (
+    NA,
     DataFrame,
     Series,
 )
@@ -76,6 +77,14 @@ def test_series_dtypes(method, data, expected_data, coerce_int, dtypes, min_peri
         tm.assert_almost_equal(result, expected)
 
 
+def test_series_nullable_int(any_signed_int_ea_dtype):
+    # GH 43016
+    s = Series([0, 1, NA], dtype=any_signed_int_ea_dtype)
+    result = s.rolling(2).mean()
+    expected = Series([np.nan, 0.5, np.nan])
+    tm.assert_series_equal(result, expected)
+
+
 @pytest.mark.parametrize(
     "method, expected_data, min_periods",
     [